Application of intelligent search data technology for cross-border e-commerce in consumer classification

At present, the era of big data represented by cloud intelligence, cloud computing, and the Internet of Things has arrived. With the help of e-commerce and logistics big data, e-commerce companies can dig deeper into potential market opportunities. Precision marketing of e-commerce user portraits based on big data is a topic worth discussing. In order to promote the cross-border e-commerce system to better serve consumers, by accurately classifying consumer data, drive business decisions from "extensive coverage" to "accurate touch", and ultimately improve user experience and enterprise operation efficiency. In the context of big data, this paper classifies consumers through the K-mean clustering algorithm, uses online shopping data and Internet data, and combines the RFM model to build a user tag system to profile users. Then, on this basis, this paper analyzes the type of the customer in each tag field, which can not only help merchants make decisions quickly and conduct precision marketing, but also provide customers with personalized services. Combined with the experiment, it can be seen that the consumer classification method proposed in this paper has a good effect in cross-border e-commerce, and the scores are all above 88 points, which verifies the scientific nature of the model. Therefore, this paper provides a reference for the intelligent development of cross-border e-commerce.